Learning Analytics and Educational Data Mining in the Service of Learning

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Abstract

Helping students acquire better self-regulated learning skills bares the promise of promoting future independent learning. However, most educational technologies fall short of interpreting, assessing, and supporting students’ use of self-regulation strategies. While the traces that students leave while solving problems in online environments are used to evaluate and support their domain-level reasoning, inferring their use of strategies remains a challenging task.

In this talk I will discuss how learning analytics and educational data mining can complement other methodologies to design and evaluate support for students’ help-seeking skills. First, I will describe an iterative process to develop an automated, unobtrusive assessment of students’ help-seeking behaviors in the context of problem-solving environments. I will then describe a series of studies that showed that adaptive feedback triggered by the automated assessment helped students to acquire better help-seeking skills in a manner that transfers to novel, unsupported, learning activities. Last, I will reflect on the use of educational data mining and learning analytics to support research in the Pasteur Quadrant (Stokes, 1997), that is, help students to acquire better metacognitive skills and improve our understanding of the nature of learning.

Date: Tuesday, 10 July 2012

Time: 2:00 – 3:00 pm

Venue: NIE, Tutorial Room @ Blk 5 Level 1 TR 501 (NIE5-01-TR501)

Biography: Dr Ido Roll

Dr Ido Roll is a research associate in the Carl Wieman Science Education Initiative and the Department of Educational and Counselling Psychology in the University of British Columbia, and a researcher in the Pittsburgh Science of Learning Center (LearnLab). Ido graduated from the Human-Computer Interaction Institute and the Program for Interdisciplinary Education Research at Carnegie Mellon University.

Ido’s research focuses on helping students to become more capable, curious, creative, and collaborative learners, using interactive learning environments. He is particularly interested in using fine-grain data to understand and promote self-regulation and scientific-inquiry skills in the context of authentic environments and tasks. Ido has published numerous papers in the fields of education and the learning sciences, cognitive science, artificial intelligence, learning analytics, educational data mining, and human¬computer interaction, and has received several best-paper awards in peer-reviewed conferences. More can be found on his website, idoroll.org).